forecastLSW-package {forecastLSW}

Forecasting for locally stationary (wavelet) time series based on the local partial autocorrelation function.

Description

This package computes forecasts for a time series with prediction errors. The forecasting methodology is designed with an underlying locally stationary wavelet model in mind. However, it is possible that the forecasting methodology will work well for other time series, including those where an underlying model is not necessarily known. Note: the methodology can work with any length of time series. The package also contains functions to display the forecasts and their prediction intervals or a fan chart, a function to evaluate the performance of the new forecasting methods and compare it to Box-Jenkins ARMA-based forecasting and a routine to identify wavelets that enable the forecasting routines to perform well.

Details

Package:	lpacf
Type:	Package
Version:	1.0
Date:	2023-04-24
License:	GPL-2

The forecastlpacf function computes forecasts of a locally stationary (wavelet) time series using the localized partial autocorrelation to help with history identification. The results of such forecasting can be printed using print.forecastlpacf or plotted with plot.forecastlpacf.

Two other useful functions are testforecast which runs some testing on forecasting some end values of a series using earlier values and compares the new forecasting with standard Box-Jenkins ARMA forecasting (visualisation via forecastpanel) and which.wavelet.best which attempts to identify which wavelet is well-suited to forecasting a particular series.

Author(s)

Rebecca Killick, Marina Knight, Guy Nason, Matt Nunes

Maintainer: Rebecca Killick <[e-mail]>

References

Killick, R., Knight, M.I., Nason, G.P., Nunes M.A., Eckley I.A. (2023) Automatic Locally Stationary Time Series Forecasting with application to predicting U.K. Gross Value Added Time Series under sudden shocks caused by the COVID pandemic arXiv:2303.07772

See Also

forecastlpacf, testforecast, which.wavelet.best

Examples

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# See examples in each of the functions' help pages linked above.
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